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对癌症模型的一致性和选择的系统方法,以实现精准医学

Jian Zou¹, Osama Shah^2,3,4, Yu-Chiao Chiu^5,6

¹Department of Statistics, School of Public Health, Chongqing Medical University, Chongqing, China.

PLoS computational biology

|January 10, 2024

概括

选择最好的癌症模型对于研究至关重要. CASCAM (Congruence Analysis and Selection of CAncer Models) 是一个新的框架,使用转录组数据来量化和选择最具代表性的癌症模型用于精准医学.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
癌症研究癌症研究

背景情况:

癌症模型,如细胞系和异种移植,对于研究至关重要,但选择最具代表性的癌症模型是具有挑战性的.
量化癌症模型与人类瘤之间的一致性仍然是一个未解决的问题.

研究的目的:

提出对癌症模型的一致性分析和选择 (CASCAM),用于验证和选择代表性癌症模型的框架.
为改善临床前研究,使用转录基因数据进行特定途径的模型选择.

主要方法:

开发了用于癌症模型选择的统计和机器学习框架 (CASCAM).
利用转录组数据用于特定路径的对应量化和拓可视化.
将CASCAM应用于侵袭性叶状乳腺癌 (ILC),三阴性乳腺癌 (TNBC) 和患者衍生的异种/有机体 (PDX/PDO) 模型.

主要成果:

CASCAM协调了欧米克数据,量化了模型一致性,并可视化了基于路径的拓关系.
确定CAMA1和UACC3133为侵入性叶状乳腺癌研究中最具代表性的细胞系.
证明了CASCAM在不同癌症亚型和模型类型中的通用性.

结论:

CASCAM提供了一种系统的方法来验证和选择最适合的癌症模型用于临床前研究.
该框架通过确保忠实地利用非人类模型来支持精准医学的进步.
CASCAM适用于任何癌症亚型,提高了癌症研究结果的可靠性.

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